17 citations · 19 across the 2 of their papers we have counts for
11 papers
Iterative VAE as a predictive brain model for out-of-distribution generalization
Victor Boutin, Aimen Zerroug, Minju Jung +1
Our ability to generalize beyond training data to novel, out-of-distribution, image degradations is a hallmark of primate vision. The predictive brain, exemplified by predictive co…
Recurrent neural circuits for contour detection
Drew Linsley, Junkyung Kim, Alekh Ashok +1
We introduce a deep recurrent neural network architecture that approximates visual cortical circuits. We show that this architecture, which we refer to as the gamma-net, learns to…
Go with the Flow: Adaptive Control for Neural ODEs
Mathieu Chalvidal, Matthew Ricci, Rufin VanRullen +1
Despite their elegant formulation and lightweight memory cost, neural ordinary differential equations (NODEs) suffer from known representational limitations. In particular, the sin…
Stable and expressive recurrent vision models
Drew Linsley, Alekh Karkada Ashok, Lakshmi Narasimhan Govindarajan +2
Primate vision depends on recurrent processing for reliable perception. A growing body of literature also suggests that recurrent connections improve the learning efficiency and ge…
Disentangling neural mechanisms for perceptual grouping
Junkyung Kim, Drew Linsley, Kalpit Thakkar +1
Forming perceptual groups and individuating objects in visual scenes is an essential step towards visual intelligence. This ability is thought to arise in the brain from computatio…
Robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks
Drew Linsley, Junkyung Kim, David Berson +1
Recent successes in deep learning have started to impact neuroscience. Of particular significance are claims that current segmentation algorithms achieve "super-human" accuracy in…